1. Field of the Invention
The present invention relates generally to oil exploration and production. More particularly, the present invention relates to using color representation of a previously computed pattern database or other attributes for visualization of geological, geophysical and engineering data for processing, analysis and interpretation for hydrocarbon exploration, development, or reservoir management on digital computers.
2. Description of the Related Technology
Exploring for hydrocarbon reservoirs is a very competitive process. Decisions affecting large amounts of capital investment are made in a time-constrained environment based on massive amounts of technical data. The process begins with physical measurements that indicate the configuration and selected properties of subsurface strata in an area of interest. The technical data include many forms of geophysical measurements including seismic signals (acoustic waves) that are introduced into the subsurface and reflected back to measurement stations on or near the surface of the earth. A variety of mathematical manipulations of the data is performed by computer to form displays that are used by an interpreter who interprets the data in view of facts and theories about the subsurface. The interpretations may lead to decisions for bidding on leases or drilling of wells.
Processing of seismic data has progressed in parallel with the increased availability and capabilities of computer hardware. Calculations performed per mile of seismic data collected have increased many-fold in the past few years. Display hardware for observation by a human interpreter has become much more versatile.
An interpreter uses data from the seismic process with some knowledge of the geology of the area being investigated. This knowledge consists of a general model of the types of depositional processes that were laying sediment, and how these processes relate to the formation of a hydrocarbon deposit. This knowledge is often called a play concept. Although the interpreter has one or a collection of play concepts, the details and specific location of specific occurrences are not known. To identify the specific occurrences, the interpreter usually visually explores the data until one or more locations exhibiting an occurrence of the play concept are identified. These occurrences are called leads. The interpreter then isolates the occurrence as an object by drawing or autotracking the outside edges of the object. The outside edge is also called its boundary representation. A coordinate system called object space can be defined on a boundary representation. A less common method is auto-tracking the entire object, including its interior. This process is called segmentation.
After an object has been created, the interpreter verifies it by analyzing it visually. This includes visually studying the shape of the exterior of the object and studying the interior. To study the exterior of the object, either the boundary representation of the object is displayed or the entire object is displayed as a set of opaque voxels, with all of the voxels outside the object either not displayed or turned transparent.
Understanding the internal characteristics of an object is usually accomplished by displaying the intersection of the objects boundary representation with a data slice then scanning through the data set one slice at a time looking at only one data type at a time. Relationships between internal details of the object and between data types have to be remembered while comparing. Some voxel based applications volume render a single data set during which all of the voxels outside the object are transparent resulting in the visual affect of the object being sculpted out of the data set. In addition, some of the voxel based applications also allow multiple data sets to be both overlain and blended through, thereby giving the same result as overlaying several translucent images on a light table. This allows multiple pieces of data to appear in a sculpted object as overlays. Opacity control allows the object to be dissected.
As more sophisticated analysis tools are developed, a large number of derivative (also called attribute) volumes are being created such as Hilbert attributes, coherence cubes, and others. In addition, pattern analysis technologies including feature, pattern, and texture extraction tools, are becoming available. The result is that there is now a growing need for simultaneous visualization of multiple data types. The multiple data types need to be viewed together with the objects, both in slice views and as volumerendered sculpted objects.
Most visualization and interpretation applications display only on type of data at a time. Some, for example VoxelGeo, overlay multiple data type as layers and then blend through the layers during visualization. They are not capable of merging the overlays into a single display that retains both the information of the individual data sets, nor do they show how they interact.
When auto-tracking based on multiple data sets, the auto-tracking is done blindly as a batch process. It is not possible to preview the data in the way that the auto-tracker perceives the data so there is no way to predict simply by looking at the data if the auto-tracking will be successful. Moreover, there is no way to know whether the auto-tracking has failed, led alone where it failed and why. Most applications perform auto-tracking only of boundary representations of the objects of interest, usually by tracking only the top or the bottom. Because all boundary representations have generally the same characteristic, being an edge, it is easy for these auto-trackers to get lost and wander from the top or the bottom of the object of interest to the top or the bottom of a different object. Some voxel applications auto-track solid objects by auto-tracking the interior. Often, to accomplish the desired results, objects need to be auto-tracked based on several types of data and then compared or combined in an external application through Boolean operations in a non-interactive manner. Objects also often need editing which is accomplished through a series of morphological operations (erosion, dilation, translation, etc.) to remove speckling due to noise and to sharpen edges. These are also usually performed in an external application. The above result in a trial and error workflow that usually takes a long time before an acceptable product is created.
What is needed is a way to perform the simultaneous display of multiple data types preserving both the information of each data type but also displaying their interaction. The interpreter needs to be able to use pattern recognition to explore data sets in a visual search, for example, of a play concept in the data to identify objects of interest. The application of mathematics, with one or more data types as input, is also needed to allow data types to be combined or modified to make visual easier.